Since widespread adoption of RETs plays a crucial role in the energy transition, the ability to understand and effectively model their diffusion is essential to orient policy decisions, set achievable targets and stimulate virtuous market mechanisms.
In recent years, the literature concerning RETs has grown considerably and expanded into several directions, including modeling and forecasting. For a recent review on some of these streams of research see [1]}.
A well-accepted approach for modeling the adoption over time of RETs relies on innovation diffusion models or growth curves. Starting from the contributions given by Marchetti at the IIASA in the '70s and '80s ([2]}, [3]}) this methodology hypothezises that energy sources are similar to commercial products that may be accepted by consumers or not. Consumers may show a different attitude in this sense, choosing whether being `pioneers' or waiting to see the decisions of others. As hypothesized by [3]}, energy consumption dynamics give rise to collective learning processes based on information sharing and imitation of others' behavior, which are best described through growth curves, like the logistic equation. From these first essential ideas, the contributions have developed in more recent times, to capture the complexity of energy system dynamics (see for instance [5]}, [6]}, [7]}), with a special focus on RETs. [7]} observed that, despite some common traits with other technologies and products, the inherent characteristics of RETs, such as low load factor, need for energy storage, small size, high upfront costs, hinder their integration into existing power systems and this explains the support provided to renewables in the form of market incentives, like feed-in tariffs.
To account for this aspect, some works have focused on measuring the effect of incentives on diffusion, by employing a successful extension of the well-known Bass Model ([9]}), the Generalized Bass Model by [10]}. In [11]} a cross-country analysis of PV diffusion was performed, highlighting the ability of the GBM in capturing the effect of market incentives in national adoption patterns. The use of the GBM also allowed a more accurate forecasting procedure through an efficient description of the nonlinear trajectory, which, in the case of renewables is often far from being smooth. In [12]} a similar analysis was conducted on the diffusion of wind power, also performing an out-of-sample forecasting evaluation with a comparison among concurrent models.
[13]} upgraded and extended previous contributions on PV diffusion, by considering the 26 countries that contributed to 99% of cumulative PV adoptions during 1992-2016. They confirmed the lack of support by the media system and showed that public incentives were often not efficiently designed in a long term perspective, with a kind of `addiction to incentive' phenomenon.
